Skip to main content
Fig. 4 | BioMedical Engineering OnLine

Fig. 4

From: Machine learning associated with respiratory oscillometry: a computer-aided diagnosis system for the detection of respiratory abnormalities in systemic sclerosis

Fig. 4

Summary of Experiment 3 (MIL5 + ML: MIL as five feature selector) and Experiment 4 (RFE5 + ML: RFE as a five feature selector)—AUCs for the best oscillometric parameter (BOP), for the best ML algorithms in experiments 3 and 4, and the best ML algorithm with oscillometric parameters (ML7). The figure indicates the best oscillometric parameter and the best ML algorithm in each case. Also, “*” indicates that there a statistically significant difference comparing to BOP (p < 0.05) and “**” (p < 0.01)

Back to article page